Conference PaperPDF Available

Semi-Transparent Building Integrated Photovoltaic Facades – Maximise Energy Savings Using Evolutionary Multi-Objective Optimisation

Maximise energy savings using evolutionary multi-objective
CHOO Thian Siong and Patrick JANSSEN
National University of Singapore, Singapore,
Abstract. The optimisation of semi-transparent building integrated photo-
voltaic facades can be challenging when finding an overall balanced
performance between conflicting performance criteria. This paper proposes a
design optimisation method that maximises overall energy savings generated
by these types of facades by simulating the combined impact of electricity
generation, cooling load, and daylight autonomy. A proof-of-concept demon-
stration of the proposed method is presented for a typical office facade.
Keywords. Multi-objective optimisation; semi-transparent building inte-
grated photovoltaic.
1. Introduction
With the current global emphasis on sustainable design, there is a trend to design
multifunctional semi-transparent building integrated photovoltaic (BIPV) facades.
Such facades use PV materials to replace conventional materials, such as glazing
systems integrated with PV cells. Semi-transparent BIPV facades can provide
good daylight availability, reduce the solar heat gain through the building enve-
lope, and also have the ability to generate electricity to supplement the building’s
electricity consumption. It has been shown that semi-transparent BIPV facades are
effective in improving energy efficiency and reducing the overall electricity con-
sumption of a building (Fung et al., 2008; Robinson et al., 2009).
The challenge of designing semi-transparent BIPV facades is to optimise the
multiple conflicting performance criteria. Unlike typical roof mounted photo-
voltaic systems, where performance is only focused on the amount of electricity
generated, the design of semi-transparent BIPV facades has an impact on a wider
R. Stouffs, P. Janssen, S. Roudavski, B. Tunçer (eds.), Open Systems: Proceedings of the 18th International
Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2013), 127–136. © 2013,
The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong, and
Center for Advanced Studies in Architecture (CASA), Department of Architecture-NUS, Singapore.
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range of factors, including solar radiation and daylight penetration into the rooms
in the building. To optimise the performance of such facades, optimisation sys-
tems can be used that leverage existing simulation tools for performance
evaluation. However, the types of simulations that are required are often complex
in their own right, and may take a significant amount of time to execute.
Optimisation systems typically need to execute these simulations many times in
an iterative manner and as a result, optimisation systems can have very long run
times, with a single run possibly taking weeks to complete (Rutten, 2011).
In order to test the run time of an evolutionary algorithm, a base-case optimi-
sation of a semi-transparent BIPV facade was conducted. A parametric model was
created and an evolutionary algorithm was used to evolve an optimised population
of designs. The evolutionary algorithm ran for almost 14 days. Such run times
clearly do not align well with a designer’s process of working.
This paper will propose an alternative method for the optimisation of semi-
transparent BIPV facades. Section two gives an overview of the proposed method,
and section three presents the results of a proof-of-concept demonstration where a
semi-transparent BIPV facade is optimised. Finally, section four briefly draws
conclusions and highlights avenues for further research.
2. Semi-transparent BIPV Facade Optimisation Method
The proposed method is based on a general design method developed by Janssen
and Kaushik (2012). This generalised method is adapted to the design of semi-
transparent BIPV facades using an evolutionary optimisation approach.
The method consists of three phases: 1) calibration, 2) optimisation, and 3) val-
idation. In the calibration phase, simulation models are selected and simulation
programs are configured and tested. Simulations that are deemed too slow are
replaced by faster proxy simulations, which are configured in order to ensure that
appropriate trade-offs are achieved between speed and accuracy. In the optimisa-
tion phase, the simulations are used within the iterative optimisation process in
order to explore design variants with improved performance. Finally, in the vali-
dation stage, the final designs from the optimisation phase are analysed and
evaluated in more detail. Any proxy simulations are now once again replaced by
the slow simulations in order to verify the performance improvements.
For the calibration phase, the first step is to decide on the performance criteria that
will be considered. For semi-transparent BIPV facades, a wide range of perform-
ance criteria could be included. However, for this paper, the focus will be on
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maximising the total electrical energy saved by the semi-transparent BIPV facade.
Such facades impact energy savings in three distinct ways: electricity generation,
cooling load, and daylight savings.
Electricity generation is the electricity produced by the PV cells in the semi-trans-
parent BIPV facade. Maximising electricity generation will reduce external
electricity consumption from the grid.
Cooling load is the amount of electricity required to cool a room to a set temperature.
Cooling load is affected by the amount of solar radiation entering through the facade.
Minimising solar radiation will reduce the electricity consumption for cooling.
Daylight savings is the amount of electricity saved by using daylight instead of arti-
ficial lighting in order to light a room to a set minimum illuminance level. The
daylight savings is affected by the daylight autonomy, which is the percentage of
occupied hours per year when the minimum illuminance level can be maintained in
a room by daylight alone (Reinhart, 2010). Maximising daylight autonomy will
also maximise daylight savings.
For each of these components, an appropriate simulation model will be chosen
and tested. If the execution time of the simulation is excessively slow, a proxy
simulation will then be developed that is faster to execute, but that nevertheless
stills has sufficient accuracy to guide the evolutionary process.
The total electricity saved is defined by the following formula:
ES = EG – CL + DS (1)
where ES is the total electricity saving (kWh.yr), EG is the electricity generated
(kWh.yr), CL is the cooling load (kWh.yr), and DS is the daylight savings (kWh.yr).
For the optimisation phase, an evolutionary algorithm will be used to evolve a
population of design variants. The evolutionary algorithm consists of three key
procedures: development, evaluation, and feedback.
The development procedure will generate design variants using a parametric
model. Typically, a Visual Dataflow Modelling (VDM) (Janssen and Chen, 2010)
system will be used to define the parametric model. Genes in the genotype are then
associated with parameters in the model.
The evaluation procedure will evaluate design variants. In this case, the evaluation
procedure will calculate the total electricity savings, which includes the electricity
generation, the cooling load, and the daylight savings.
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The feedback procedure will kill design variants that perform badly and will repro-
duce design variants that perform well. Avariety of different strategies can be used
for selection and reproduction.
For the validation phase, the best designs emerging from the evolutionary process
are analysed and re-simulated. At this final stage, if possible the proxy simulations
are discarded, and instead the more accurate but slower simulations are used.
Performance characteristics of the final design variants can then be verified.
3. Demonstration
To demonstrate the feasibility of the proposed method, an experiment was conducted.
The experiment involves optimising the pattern of PV cells on a semi-transparent
BIPV facade in order to maximise the total electricity savings. The PV pattern
affects both the solar radiation and the daylight penetrating into the room through
the glazing, and it will therefore have an impact on all three components of the elec-
tricity savings calculation: electricity generation, cooling load, and daylight savings.
A typical north oriented office space for one person occupancy with 4m (width)
x 4m (depth) x 3m (height) is modelled for the experiment, as shown in Figure 1
(left). The facade is separated into 4 zones (Figure 1, left): vision glass panel A, B,
C and spandrel glass panel. Each zone is independent from each other. The PV cell
Figure 1. (left) Simulation model with sensors (a = 1.50m, b = 0.85m, c = 0.50m), (right)
schematic of cell arrangement for semi-transparent BIPV façade.
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pattern for each independent zone is defined by three parameters (Figure 1, right):
cell height, cell width, and cell spacing. Cell height and width vary from 5 – 15.5cm
at 0.5cm steps but are independent from each other. Cell spacing varies from 0.5 –
5cm at 0.5cm steps. All the cells of the semi-transparent BIPV facades will be sim-
ilar in shape. The pattern occupies a facade with a height and width of 4m.
The main tool that is used is Grasshopper (Rutten, 2011), a plugin for the
Rhinocerous computer aided design modelling software (McNeel, 2010).
Grasshopper (Rutten, 2011) is a VDM system that allows designers who are not
trained in scripting to quickly generate parametric models. A number of specialist
Grasshopper components are used for running optimisation algorithms and for
executing simulations.
For running optimisations, the Galapagos component is used (Rutten, 2011).
This component is an evolutionary optimisation solver which can be used to opti-
mise designs for a single performance criterion.
For executing cooling load and daylight autonomy simulations, the DIVA
component is used (Jakubiec and Reinhart, 2011). DIVA links to the EnergyPlus
(Crawley et al., 2001) and Daysim (Reinhart, 2010) simulation programs.
EnergyPlus is a building performance simulation software that is based on fun-
damental heat balance principles. Daysim is a Radiance-based daylighting
analysis tool that couples the Radiance (Ward and Shakespeare, 1998) algorithms
with a daylight coefficient approach that efficiently simulates illuminance distri-
bution in a year without executing thousands of individual ray tracing runs for all
sky conditions which is impractical.
EnergyPlus is used to simulate cooling loads and Daysim is used to simulate
daylight autonomy. In both cases, an EnergyPlus weather data file (NREL, 2012)
for Singapore is used. The daylight autonomy is then used as a basis for calculat-
ing daylight savings.
For the calibration phase, each of the three components of the total electricity sav-
ings calculation will be considered in turn.
3.3.1. Electricity generation
The annual electricity generation of a photovoltaic module can be simulated using
EnergyPlus. Ideally, it would simulate with the equivalent one-diode model
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(Crawley, 2001). However, this model requires the electrical characteristic of a
module to be known in advance. For an untested type of photovoltaic module, the
electrical characteristic can only be obtained by fabricating the module. This poses
a problem when designing semi-transparent BIPV facades with non-standard PV
cell patterns, since each design option would need to be fabricated. Hence a sim-
pler model needs to be used that is independent of the electrical characteristics.
Note that the reason for using this simpler model is due to lack of input informa-
tion rather than simulation speed.
EnergyPlus already has such a simple model, as an alternative to the equiva-
lent one-diode model (EnergyPlus, 2011). In this research, we propose to use the
simple model. The mathematical equation used is as follows:
P= A
sx fa x Gtx effcell x effinvert (2)
where P is the electrical energy produced by photovoltaic (kWh), Asis the net area
of photovoltaic module (m2), Fais the fraction of surface area with active solar
cells, Gtis the total annual solar radiation energy incident on PV array (which is set
at 561 kWh.m–2), effcell is the semi-transparent BIPV facades module efficiency
(which is set at 12%) and effinvert is the inverter efficiency (which is set at 90%).
In order to verify the accuracy of the simple model, a set of commercially pro-
duced photovoltaic modules (for which the electrical characteristics were already
known) were simulated using both the equivalent one diode model and the simple
model, and the results were then compared. In total, simulations for 16 different
modules were carried out for the four different cardinal directions. The annual
electricity generation was simulated with EnergyPlus for both models
(EnergyPlus, 2011). The trend-line of 64 pairs of results for the simple and the
equivalent one-diode models were plotted in Microsoft Excel. The electricity gen-
eration for the simple model resulted in an R2correlation of 0.98. This shows that
the simple model has a good correlation and can be used in place of an equivalent
one-diode model.
3.3.2. Cooling load
The cooling load for the room can also simulated using EnergyPlus. For cooling
load, the study is interested in the cooling load affected by heat gain through the
semi-transparent BIPV facades hence internal heat gains from lights, equipment
and occupants have been set to 0. Default materials from the material library in
DIVA are used. With reference to Figure 1 (left), the walls, floor and ceiling are
assigned as “adiabatic” and spandrel glass panel are assigned as “opaque spandrel
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A window module is defined to represent a typical 6mm thick clear glass win-
dow with U-value of 5.8, solar heat gain coefficient (SHGC) of 0.82 and visible
transmittance (VT) of 0.88 (Pilkington, 2010). For the photovoltaic cells, there are
two different approaches with different trade-offs between speed and accuracy.
The slower more accurate approach to modelling the PV cells is to assign them as
external shading elements. Each cell is assigned a solar reflectance of 0.1 and vis-
ible reflectance of 0.1. The amount of solar radiation affected by the shading from
photovoltaic cells is calculated for each photovoltaic cell and for each time-step
for an entire year in the simulation. Each time step calculation is different from the
next because each value is dependent on the time and location of the sun. A range
of 156 to 4224 photovoltaic cells with different patterns are input as external shad-
ing elements for each simulation. This large number of shading elements causes
the simulation to run relatively long, with the longest simulation taking approxi-
mately 30 minutes. This caused the optimisation of the base case, mentioned in
Section 1, to run for almost 14 days.
A faster proxy simulation is therefore proposed that uses a less accurate
approach to the modelling of the PV cells. With this approach, the solar heat gain
coefficient (SHGC) and visible light transmittance (VLT) for the facade are
adjusted to take into account the effect of the PV cells. The equations for SHGC
and VT used in the proxy simulation are shown below:
SHGCsst = Apv/Asst x SHGCst (3)
VTsst = Apv/Asst x VTst (4)
where SHGCsst is the solar heat gain coefficient of semi-transparent BIPV facade
(Wm–2K–1), SHGCst is the solar heat gain coefficient of the vision glass panel (which
is part of the semi-transparent BIPV facade) without photovoltaic cells (Wm–2K–1),
VTsst is the visible transmittance of the semi-transparent BIPV facade (Wm–2K–1),
VTst is the visible transmittance of the vision glass panel (which is part of the semi-
transparent BIPV facade) without photovoltaic cells (Wm–2K–1), Apv is the area of
photovoltaic cells (m2) and Asst is the area of semi-transparent BIPV facade (m2).
In order to check the accuracy of the proposed proxy simulation, a total of 164
cooling load simulations for different BIPV facades were conducted using both
the slow simulation and the proxy simulation. The trend-line for both the slow and
proxy simulations were plotted in Microsoft Excel, and an R2correlation of 0.93
was calculated. This shows that the proposed proxy simulation for cooling load
has a good correlation and can be used in place of the slower more detailed cool-
ing load simulation.
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3.3.3. Daylight Savings
Daylight savings is calculated based on the daylight autonomy for the room,
which can be simulated using Daysim. Since Daysim is already an optimised sim-
ulation method (Reinhart, 2010), the simulation executed relatively quickly and
there was no need to create a proxy in this case.
With reference to a recent study on various lighting standards around the world
by Halonen et al. (2010), it was found that minimum illuminance for interior
spaces ranges from 200lx to 500lx. Hence, for the simulation of daylight auton-
omy, the minimum illuminance level of 500lx is set for the simulation. Working
hours are set from 9:00 to 18:00. A 3 x 3 nodal grid of daylight sensors are drawn
0.85m from the floor and 0.25m away from the vertical walls (Figure 1, left).
Since daylight autonomy is more critical for areas further way from the windows,
only the back 2 rows of 6 daylight sensor nodes are used for the daylight auton-
omy simulation.
Default materials from the material library in DIVA are used for the simulation.
Based on research done by Protogeropoulos and Zachariou (2010) which shows
that a typical photovoltaic module has a reflectance of below 10%, the photo-
voltaic layer is assigned a reflectance of 10%.
The following settings were used in DIVA/Daysim: ab = 2, ad = 1000, as = 20,
ar = 300 and aa = 0.1, where ab is ambient bounce, ad is ambient resolution, ar is
ambient resolution and aa is ambient accuracy. The detailed explanation of the set-
tings is beyond this paper. They can be referred to in the Radiance manual.
Daylight savings were then calculated according to the following formula:
DS = (DAsim/100) * LPB * FA * WH (5)
where DS is the total daylight savings (kWh.yr–1), DAsim is the simulated daylight
autonomy (%), LPB is the lighting power budget (kW.m–2), FA is the floor area of
simulation model (which is 16m2) and WH is the working hours per year. LPB is
set based on the Code of Practice (SPRING, 2006) which recommends an LPB for
offices of 0.015kW.m–2. WH is set based on a 5 work days per week with 9hrs of
work per day, which results in 2,340hrs per year.
The evolutionary solver, Galapagos, is used for the optimisation. Galapagos is
executed with a population of 30, initial boost of 2%, and the maintain level is set
at 10% and inbreeding at 75%. The system was executed on a single computer
with an i5 Intel core CPU of 3.5GHz with 8GB of RAM, on a 64 bits Windows.
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Figure 2. Optimised design.
Galapagos ran for 2 days, 17 hours. Compared to the base case which ran for
almost 14 days, this method reduced the run time by 80%. The optimised design
that was found at the 544th iteration is shown in Figure 2. The orange dot repre-
sents the ideal design performance and the red dot represents the design with the
best performance. The fast mode optimisation process completed 1471 iterations
of simulations. The optimised design is similar to the optimised design found in
the base case.
In the final validation phase, a set of designs from the Pareto front were selected
and analysed. In order to verify the performance improvements in cooling load,
these designs were re-simulated using the slow cooling load simulation. In all
cases, the results from the slow mode simulation confirmed the performance
improvements. In the case of the example shown in Figure 2, the optimised final
design showed an overall improvement of 61% of the overall electricity over the
initial design.
4. Conclusion
A design method for the multi-objective optimisation of semi-transparent BIPV
facades is proposed. The overall objective is the maximisation of energy savings,
but this objective embodies three distinct sub-objectives, defined as the maximi-
sation of energy generation, the minimisation of cooling loads, and the
maximisation of daylight savings. The method also significantly reduces the opti-
misation runtime by using a proxy simulation for calculating cooling loads.
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The proposed design method simplifies the task of finding an overall balance
performance for semi-transparent BIPV facades, especially where conflicting per-
formance criteria may prove to be challenging to explore manually. This design
method need not be restricted to the design of semi-transparent BIPV facades but
can also be extended to optimise other design elements in a building and also
include other performance criteria.
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Reflectivity measurements off the surface of PV modules and other reflecting surfaces were performed in the laboratory. The measurements were done using dedicated instruments of very high accuracy and focused on the visible light wavelength, i.e. 380nm to 700nm. The results proved that the reflections coming from PV modules are significantly less intense than others resulting from other surfaces, particularly those coming from vehicles. At large angles of incidence, when reflectivity is maximised, PV modules show advantageous behaviour due to the high absorption compared to other surfaces like car windshields and paints.
Semi-transparent photovoltaics (STPV) have a large potential for integration in fenestration systems, adding the option of solar electricity production while still allowing for satisfaction of daylight needs. This paper studies the potential of using such a technology and examines the impact of changing the photovoltaics (PV) area ratio (ratio of PV coverage to fenestration area) on the STPV façade. It includes a preliminary verification of the workplane illuminance model through comparison with measured data from an experimental office with a specially built full- scale prototype of a window with spaced solar cells in its upper section. The paper will address the issue of optimizing the PV area ratio for a simplified model based on a typical office in Montreal with a 3- section façade. The effect of changing orientation and PV efficiency on the overall net electricity generation (including the lighting load, heat gain from the artificial lighting, and the output of the PV) is presented. The annual simulation results show that a façade with integrated STPV has the potential to improve overall energy performance when compared with opaque PV due to the significant daylighting benefits even at low transparency ratios.
This paper presents a one-dimensional transient heat transfer model, the Semi-transparent Photovoltaic module Heat Gain (SPVHG) model, for evaluating the heat gain of semi-transparent photovoltaic modules for building-integrated applications. The energy that is transmitted, absorbed and reflected in each element of the building-integrated photovoltaic (BIPV) modules such as solar cells and glass layers were considered in detail in the SPVHG model. Solar radiation model for inclined surface has been incorporated into the SPVHG model. The model is applicable to photovoltaic (PV) modules that have different orientations and inclinations. The annual total heat gain was evaluated by using the SPVHG model. The impacts of different parameters of the PV module were investigated. It was found that solar heat gain is the major component of the total heat gain. The area of solar cell in the PV module has significant effect on the total heat gain. However, the solar cell energy efficiency and the PV module's thickness have only a little influence on the total heat gain. The model was also validated by laboratory tests by using a calorimeter box apparatus and an adjustable solar simulator. The test results showed that the simulation model predicts the actual situation well.
Radiance is a collection of approximately 50 programs that do everything from object modeling to point calculation, rendering, image processing and display. This is the definitive reference on the radiance lighting simulation and rendering system.
Energy: Photovoltaic, Input-Output Reference, EnergyPlus Documentation
  • Energyplus
EnergyPlus: 2011, Energy: Photovoltaic, Input-Output Reference, EnergyPlus Documentation, U.S Department of Energy, Berkeley.
DIVA-FOR-RHINO 2.0: Environmental parametric modeling in Rhinoceros/Grasshopper using Radiance, Daysim and EnergyPlus, Building Simulation
  • A Jakubiec
  • C Reinhart
Jakubiec, A. and Reinhart, C.: 2011, DIVA-FOR-RHINO 2.0: Environmental parametric modeling in Rhinoceros/Grasshopper using Radiance, Daysim and EnergyPlus, Building Simulation 2011, Sydney.
Available from: NREL's weather data website
  • R Mcneel
McNeel, R: 2010, Rhinoceros -NURBS Modelling for Windows (version 4), McNeel North America, Seattle. NREL: 2012, "Weather Data". Available from: NREL's weather data website <http://apps1.eere.> (accessed 15 October 2012).